Fraud Detection using Python

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Richard Ball, PhD

2:05:52

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1. Introduction to the course

1. Our exciting Fraud Detection project demo!.mp4

02:04

2. Installing the required software.html

2. Introduction to Fraud Detection

1. What is Anomaly Detection.mp4

10:54

2. What is Fraud Detection.mp4

07:03

3. Exploring the Credit Card Fraud Data Set.mp4

04:59

4. Managing Labels in a Fraud Detection Context.mp4

05:03

5. Sampling with a Class Imbalance.mp4

10:40

6. Introduction to Fraud Detection Quiz.html

3. Training a Supervised Learning Model

1. Training a Baseline Logistic Regression Model using scikit-learn.mp4

09:26

2. Improving the Logistic Regression Model through Hyperparameter Selection.mp4

06:52

3. Interpreting the Logistic Regression Model.mp4

11:05

4. Training an XGBoost Model.mp4

04:42

5. Improving the XGBoost Model through Hyperparameter Selection.mp4

04:06

6. Interpreting the XGBoost Model.mp4

02:36

7. Training a Supervised Learning Model Quiz.html

4. Performance Metrics for Fraud Detection

1. Understanding the Cost of Misclassification.mp4

07:45

2. The Accuracy Paradox.mp4

02:57

3. Implementing Performance Metrics in scikit-learn.mp4

08:03

4. Performance Metrics for Fraud Detection Quiz.html

5. Optimal Model Selection

1. Threshold Optimization using Performance Metrics.mp4

06:57

2. Threshold Optimization using Total Cost of Fraud.mp4

04:38

3. Introduction to Streamlit.mp4

01:34

4.1 app.zip

4.2 error_df.csv

4.3 eval.zip

4. Building a Threshold Simulation for Visual Inspection.mp4

08:31

5. Optimal Model Selection Quiz.html

6. Strategies for Improving Model Performance

1. Up-sampling the Minority Class with SMOTE.mp4

05:57

2. Strategies for Improving Model Performance Quiz.html

Description

Build an effective machine learning project to detect instances of financial crime.

What You'll Learn?

Use Python to analyze a sample credit card fraud data set
Train and improve various supervised machine learning models to detect fraud
Generate and interpret performance metrics relevant to fraud detection
Select an optimal classification model based on various criteria
Apply various strategies for improving the performance of your fraud detection models

Who is this for?

Any fraud analysts interested in machine learning.

Anyone interested in learning about how to apply machine learning to detect fraud.

Data scientists looking to train a fraud detection model for their organizations.

More details

Description
If you're interested in detecting fraud using machine learning, then this course is for you!
Fraud is a massive problem for many modern organizations, as bad actors are becoming increasingly sophisticated both in methodology and technical ability. Detecting fraud is therefore an important problem that is never going to be completely solved. By taking this course, you'll be levelling up with a hireable skillset that is likely going to be relevant and for many years to come.
This course was developed by myself, a Principal Data Scientist with a PhD in Machine Learning and real-world expertise in deploying production machine learning models for detecting fraud in the financial services industry.
In this course, students will be introduced to the problem of fraud in industry, and how it can be solved via the introduction of various machine learning approaches. I will walk you through an example fraud detection problem, where you will get hands-on exposure to building models using Python. This will include navigating the challenging problem of fraud, where special consideration needs to be given to the highly imbalanced nature of the data.
The lessons covered in this course include:
Lesson 1 - Introduction to fraud detection: anomaly detection, class imbalance
Lesson 2 - Training a supervised machine learning model to detect fraud: logistic regression, XGBoost, performance improvement through hyperparameter optimization
Lesson 3 - Performance metrics for fraud detection: confusion matrix, cost of misclassification, accuracy paradox, implementing metrics in scikit-learn
Lesson 4 - Optimal model selection: threshold optimization using performance metrics, threshold optimization using cost of fraud, introduction to Streamlit, building a threshold simulator for visual inspection
Lesson 5 - Strategies for improving model performance: sampling techniques
Each lesson builds on the practical knowledge achieved in the prior lessons, allowing for students to produce a completed end-to-end project as the final output of the course. This project could serve as an important part of a student's portfolio of projects, assisting with their job search and professional development endeavors.
The Python technology stack used within this course includes the following: pandas, numpy, matplotlib, scikit-learn, seaborn, XGBoost, Streamlit and imblearn.
Who this course is for:
Any fraud analysts interested in machine learning.
Anyone interested in learning about how to apply machine learning to detect fraud.
Data scientists looking to train a fraud detection model for their organizations.

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Principal Data Scientist and PhD in Machine Learning, specializing in the application of deep learning for anomaly detection in a financial services context. I'm currently building cloud-based production machine learning systems for detecting fraud at scale. My interests include fraud and anomaly detection, network analysis, model interpretability, and graph neural networks.

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